vibescience-mcp

by AntonIXO

Overview Schema Related Servers Score Discussions

Python

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Server Quality Checklist

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Latest release: v0.1.0
Disambiguation5/5
Each tool has a distinct purpose: registration, listing, core experimental workflow, paper linking, and analysis. No two tools appear to overlap; even 'recall' is uniquely positioned as a pre-mortem gate.
Naming Consistency3/5
Most tools follow a verb_noun snake_case pattern (register_diagnostic, list_problems, propose_hypothesis). However, a few deviate: 'recall', 'causal_map', 'calibration', and 'reindex' use single verbs or nouns, creating minor inconsistency.
Tool Count5/5
With 17 tools, the set is well-scoped for a research experiment tracking system. Each tool serves a necessary role in registration, experimentation, analysis, or maintenance, without feeling bloated or sparse.
Completeness4/5
The tool set covers the full lifecycle (register, recall, problem definition, hypothesis proposal, experiment execution, diagnostics recording, closure, and analysis). Minor gaps include no explicit delete or update tools, but the core workflow is complete.
Average 3.9/5 across 17 of 17 tools scored. Lowest: 2.8/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 25 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
Add a glama.json file to provide metadata about your server.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations are minimal (idempotentHint=false, destructiveHint=false). The description adds that the intervention is 'reusable' and contributes to causal map deltas, but it does not clarify idempotency behavior, what happens on duplicate names, or side effects beyond 'register'. This leaves ambiguity about mutability and duplicates.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Description is two sentences, front-loads the action, and adds a second sentence of context. No wasted words. However, it could be improved by including parameter hints without increasing length significantly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With 4 parameters, 0% schema coverage, and an output schema (not shown), the description is too sparse. It does not explain the purpose of each parameter, expected inputs, or the output format, leaving significant gaps for proper invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%: no parameter descriptions in the schema. The description mentions only 'name' implicitly via example but provides no information about the other three parameters (id, topic_tags, description) or their roles, defaults, or constraints. This forces the agent to guess parameter semantics.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the tool registers a named, reusable change with an example ('unfreeze-projector'). The verb 'register' distinguishes it from sibling tools like 'list_interventions' (listing) and 'register_diagnostic' (different resource). However, it does not explicitly differentiate from all siblings, missing a chance for absolute clarity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Description implies the tool is used to create interventions that are reused across hypotheses for causal map attributions. It does not provide explicit guidance on when to use versus alternatives, nor does it mention prerequisites or when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds context about citing from hypotheses, which is beyond annotations. However, it lacks details on idempotency, deduplication, or validation. Annotations already indicate non-destructive behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences with no fluff. Every word adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 5 parameters and no schema descriptions, the description is too minimal. It does not cover parameter details or provide sufficient context for correct usage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description does not explain any parameters or their constraints. It fails to compensate for the lack of schema documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool adds an external reference with metadata and key claims, and mentions citing from hypotheses. This provides a specific verb and resource, but does not explicitly differentiate from sibling tools like link_paper.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for adding references and citing them, but does not provide explicit guidance on when to use this tool versus alternatives, nor when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations already declare readOnlyHint=true and idempotentHint=true, so the agent knows it is a safe, read-only operation. The description adds no additional behavioral context (e.g., response format, scope of 'basis', or any side effects), thus providing no value beyond the annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with no extraneous words. It front-loades the verb and resource, making it immediately actionable. Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has no parameters, has an output schema to describe return values, and is simple, the description is mostly adequate. However, it could briefly clarify what 'diagnostic basis' means to avoid ambiguity, but the presence of the output schema compensates.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With zero parameters and 100% schema coverage, the input schema is already fully defined. The description does not need to explain parameters. Following guidelines, a baseline of 4 is appropriate for parameterless tools.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'list' and the resource 'diagnostic basis', indicating a retrieval operation. It is distinguishable from siblings like 'register_diagnostic' (creation) and 'list_interventions' (different resource). However, the term 'diagnostic basis' is somewhat vague and could be more precise.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like 'list_interventions' or 'list_problems'. It lacks any mention of prerequisites, context, or when not to use it, leaving the agent without decision support.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare idempotentHint=true and destructiveHint=false, so the description adds minimal behavioral context beyond mentioning it adds a 'cites edge'. This is consistent but not enriched.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that conveys the entire purpose without unnecessary words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple with two parameters and an output schema, but the description does not clarify what 'entity_id' refers to (problem or hypothesis). It is adequate but could be slightly more explicit.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, and the description does not explain the parameters. 'paper_id' and 'entity_id' are self-explanatory from names, but the description adds no further semantic value or constraints.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'link' and the objects 'paper to a problem or hypothesis', with the specific action 'adds a cites edge'. This uniquely identifies the tool's purpose and distinguishes it from sibling tools like add_paper or create_problem.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. There is no mention of prerequisites, context, or when not to use it, leaving the agent without clear decision criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations declare idempotentHint=true and destructiveHint=false. The description adds behavioral detail: never commits for you, returns a suggestion, and that verdict is computed. This goes beyond annotations without contradiction.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is three sentences, front-loaded with the key verb 'Finalize', and every sentence adds distinct information. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given complexity (computation, propagation, verdict) and presence of output schema, the description covers core logic. However, it does not explain verdict conditions or output format details, leaving some gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description should explain parameters, but it only indirectly refers to experiment_id (via context) and ignores the 'notes' parameter. No parameter-level detail is provided.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly specifies the action ('Finalize'), the computed outputs (observed_effects, prediction_match, verdict), and the propagation to hypothesis. It distinguishes from sibling tools like start_experiment and propose_hypothesis by detailing unique behavior.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides usage context (e.g., 'you cannot assert confirmed', verdict is computed) and hints at workflow (e.g., returns commit suggestion). However, it lacks explicit when-not-to-use instructions or direct alternatives from the sibling list.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations are minimal (idempotentHint: false, destructiveHint: false). The description adds that diagnostics are 'fixed' and 'deliberate', implying immutability, but does not disclose side effects, uniqueness constraints, or error conditions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Three concise sentences with no fluff. Front-loaded with the primary action and context, each sentence earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Provides key context for a registration tool (when to use, purpose) but missing details about uniqueness, return value (output schema exists but undocumented), and error handling.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is low (20%). The description does not explain individual parameters like 'unit', 'direction', or 'id', relying on schema defaults without adding meaningful context.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Register a named, fixed, measurable metric') and its purpose ('comparability across experiments'), setting it apart from siblings like list_diagnostics and record_diagnostics.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly says 'Do this before predicting on a metric', providing clear temporal usage context. No explicit exclusions or alternatives, but the description implies when it is appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint and idempotentHint. The description adds no additional behavioral traits beyond 'Fetch', such as error handling or rate limits. It neither contradicts nor significantly supplements the annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
A single sentence with no redundant words. Information is front-loaded and efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple (one required param, no nested objects) and has an output schema. The description covers the essential purpose and input, making it complete for an AI agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description only says 'by id', which adds minimal meaning beyond the schema's parameter name. However, the single parameter is self-explanatory, so a baseline of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Fetch a single problem') and the resource ('by id'). It effectively distinguishes from sibling tools like list_problems (multiple) and create_problem (creation).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies when to use (when you have a specific id), but offers no explicit guidance on when not to use or alternatives. No exclusions or context for decision-making.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations declare readOnlyHint and idempotentHint true, so agent knows it's safe. Description adds filter value ranges (status enum) which goes beyond annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence, front-loaded with action and resource, no extraneous words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With annotations and output schema present, description covers key aspects. Could add more on result ordering or pagination but adequate for a simple list tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema has 0% description coverage. Description provides meaning for status (enum values) and tags but lacks format or examples. For two parameters, this is minimal compensation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the tool lists problems and mentions optional filters by status and tags. It distinguishes from siblings like get_problem (single) and create_problem.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool vs alternatives (e.g., get_problem for single problem, list_interventions for interventions). Context is implied by the tool name and sibling list.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate non-idempotent and non-destructive behavior. The description adds that it 'registers' a problem (implying creation), but no further behavioral details (e.g., effect on existing data, permissions). Minimal added value beyond annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, front-loaded with the core purpose, and no superfluous words. Every sentence serves a clear function.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Provides essential usage guidance but lacks details on parameter semantics, return values (despite output schema exists), and what 'register' entails. For a creation tool with six parameters, more context (e.g., uniqueness constraints, effect on related data) would be beneficial.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description provides no explanation of the six parameters (e.g., 'title', 'paper_refs', 'topic_tags'). Only the required 'title' is implied by context. The description fails to compensate for the lack of schema documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool registers an 'open research question / failure mode' and distinguishes it from sibling tools like 'recall' and 'list_problems'. The verb 'register' combined with the resource 'problem' is specific and unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly advises 'Do this once per distinct problem' and instructs to 'Use `recall` first to check it isn't already logged', providing clear when-to-use and when-not-to-use guidance with a named alternative tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare idempotentHint=true and destructiveHint=false. The description adds that re-recording overwrites (consistent with idempotency) and that 'Observed deltas/directions are derived', providing behavioral context beyond annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise with two sentences, no redundancy. Every word adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Has output schema (so return values need not be explained). The description covers key behaviors: recording, idempotency, derivation. For a simple recording tool with 2 params, this is sufficiently complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 50% (experiment_id lacks description). The description does not add parameter-level details; it only states the general purpose. Measurements param has schema description but no additional context from description. Given partial coverage, the description provides minimal added value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Record' and resource 'diagnostics', specifying 'before/after values'. This distinguishes it from sibling tools like register_diagnostic (which registers new diagnostics) and list_diagnostics.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description mentions idempotency (overwriting on re-record) but does not provide explicit guidance on when to use this tool versus alternatives, nor when not to use it. The context is implied but incomplete.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Behavioral traits are disclosed beyond the minimal annotations: it explains the auto-read of git HEAD if git_ref is omitted, and clarifies that the server stores verdicts not loss curves. This adds valuable context that annotations (idempotentHint=false, destructiveHint=false) do not provide. No contradiction with annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences long with no wasted words. The first sentence states the purpose efficiently, and the second adds two key behavioral details. It is front-loaded and every sentence earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 5 parameters (1 required), minimal annotations, and an output schema, the description covers the key aspects: purpose, git_ref behavior, and external run relationship. It does not explain the output format, but since an output schema exists, that is acceptable. It is complete enough for an agent to use the tool correctly, though it could mention that it creates a new experiment record.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 40% (2 of 5 parameters have descriptions in schema). The description adds meaning for git_ref (auto-read from HEAD) and external_run (reference to W&B/MLflow run). However, for the other parameters (id, config_note, hypothesis_id), no additional semantics are provided beyond the schema. This partially compensates for the low coverage but not fully.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Begin testing a hypothesis.' It uses a specific verb (begin) and resource (hypothesis). It also distinguishes itself from sibling tools like 'propose_hypothesis' by indicating it initiates the testing phase. The added details about git_ref and external runs further clarify its scope.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides context about when to use the tool (when you want to begin testing) and mentions the external run reference, but it does not explicitly contrast with sibling tools or state when not to use it. There is no guidance on prerequisites or alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and idempotentHint; description adds no extra behavioral traits beyond confirming it is a list operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Single, efficient sentence with no wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no parameters and an output schema available, the description adequately covers the tool's purpose, though it could optionally define 'interventions'.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
No parameters exist; description adds no information beyond schema, which is empty, but baseline for 0 params is 4.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states verb 'list' and resource 'registered interventions', distinguishing from sibling such as 'register_intervention'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The purpose is clear and implies usage context, but no explicit when-not or alternatives are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark the tool as read-only and idempotent. The description adds context about the report's computation and interpretation, which goes beyond the annotations. No contradictions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Three concise sentences, each with a clear function: definition, motivational question, scope instructions. No superfluous text.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the optional parameters and presence of an output schema, the description fully explains what the tool does and how to filter. No missing information needed for correct invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With no schema description coverage (0%), the description compensates by naming the three scope parameters (diagnostic, tag, intervention) and explaining their role in filtering. Does not specify formats or constraints, but provides essential meaning.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Clearly defines the tool as a prediction-accuracy report, specifying it computes the fraction of experiments where predicted direction equals observed direction. The description directly answers 'where is my intuition miscalibrated?', distinguishing it from sibling tools like 'recall' or 'list_interventions'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
States the tool can be scoped by diagnostic, tag, or intervention, providing clear usage context. Does not explicitly mention when not to use or alternatives, but the purpose is sufficiently specific for an AI agent to infer appropriate usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnly and idempotent behavior. The description adds context that it's an aggregated, non-destructive view, which is consistent. No contradictions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences: first defines the core function, second adds scoping. Front-loaded, no fluff. Highly efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the output schema exists, the description sufficiently covers what the tool does and when to use it. For a read-only data retrieval tool, it's complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is only 33%, but the description explains the purpose of tag and problem_id for scoping, adding value beyond the schema. emit_canvas is described in the schema. This compensates for the low coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states that the tool provides an aggregated view of intervention effects on diagnostic subgraphs across experiments, acting as a meta-analysis. It uses specific terms like 'Δdiagnostic subgraph' and 'mini meta-analysis', distinguishing it from sibling tools like register_diagnostic or list_interventions.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for meta-analysis of intervention effects and explicitly mentions scoping by problem_id or tag. It lacks explicit when-not-to-use or alternative tools, but the purpose is clear enough for a knowledgeable agent.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Description adds 'files are truth' context beyond annotations (idempotentHint, destructiveHint). Confirms safe to call and produces identical results.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence, front-loaded with action, no wasted words. Highly concise and clear.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a no-parameter, idempotent tool with an output schema, the description fully explains purpose, safety, and behavior. No missing details.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
No parameters, baseline 4. Description does not need to add parameter info as schema is empty and coverage is 100%.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states 'Rebuild the disposable SQLite index from the markdown vault' which is a specific verb and resource. Distinguishes from sibling tools focused on diagnostics, interventions, problems, etc.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly says 'Safe to call anytime', providing clear guidance on when to use. No need for when-not as no side effects. No alternatives mentioned, but siblings are unrelated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations only provide destructive=false and idempotent=false. Description adds crucial behavioral details: requires committed predicted_effects on registered diagnostic, explains why (calibration signal), and how to revise (supersedes). No contradictions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Three sentences with front-loaded purpose. Every sentence provides essential guidance without redundancy. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 11 parameters, 3 required, with output schema, the description covers the core workflow well. However, many optional parameters are not described, and the low schema coverage means some details are missing. Still comprehensive for the main task.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is very low (18%). The description adds meaning for 'predicted_effects' (required array with structure) and 'supersedes', but many parameters (id, plan, papers, etc.) remain unexplained. Partially compensates for low schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Propose an explanation/intervention for a problem' with a specific verb+resource. It distinguishes from sibling tools like register_diagnostic and register_intervention by emphasizing predicted_effects commitment.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly provides when to use (propose hypothesis for a problem), prerequisite (registered diagnostic), and alternatives ('Call `recall` first', 'pass `supersedes`'). Also states consequences if not followed ('rejected otherwise').
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations indicate read-only and idempotent. Description adds: returns ranked results with fail reason, diagnostic delta, and calibration notes. Adds value beyond annotations without contradiction.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Description is front-loaded with imperative, then explains output. It is detailed but not overly verbose. Slightly long but each sentence adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the presence of an output schema and high schema coverage, the description provides sufficient context on when, why, and what to expect. Complete for the tool's complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 75% (3 of 4 params have descriptions). The description does not add parameter-specific meaning beyond the schema. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it returns ranked past hypotheses/experiments, with refuted ones ranked top and reasons. The imperative 'CALL THIS FIRST' distinguishes it from siblings like propose_hypothesis and start_experiment.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly instructs 'CALL THIS FIRST, before proposing any hypothesis' and specifies the post-use action: propose a new hypothesis. Provides clear when and how to use, preventing re-walking dead ends.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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